Generally, non-contact gaze input devices require calibration before using. However, it is difficult for people with severe multiple disabilities who is mentally challenged and have difficulty in moving their bodies as they wish, to move their eyes according to the instructions. It takes time and effort to use a dedicated device, which makes them difficult to express intentions by looking. In this study, we constructed a real-time gaze area estimation system that does not require calibration while maintaining the resolution required for challenged people. Using a usual web camera, gaze area estimation was realized by learning eye and facial appearance with Convolutional Neural Network (CNN). Next, 36 gazing points were set on the screen, and the evaluation experiments were performed by changing the relative distance between the camera and the face, and the posture angle of the face. In conclusion, it was confirmed that practical accuracy of the basic posture was maintained up to 1,200 mm for the distance, 100 mm downward in terms of position. The results for the posture angle were obtained for one subject only, but it was shown that the accuracy was maintained up to 10 degrees for the yaw, 15 degrees for the pitch, and 15 degrees for the roll angle, respectively.